Ensemble hidden Markov models with application to landmine detection

Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models

We propose a real-time software system for landmine detection using ground-penetrating radar (GPR). The system includes an efficient and adaptive preprocessing component; a hidden Markov model(HMM-) based detector; a corrective training component; and an incremental update of the background model. The preprocessing is based on frequency-domain processing and performs ground-level alignment and ...

Ensemble Learning for Hidden Markov Models

The standard method for training Hidden Markov Models optimizes a point estimate of the model parameters. This estimate, which can be viewed as the maximum of a posterior probability density over the model parameters, may be susceptible to over-tting, and contains no indication of parameter uncertainty. Also, this maximummay be unrepresentative of the posterior probability distribution. In this...

Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series

Intrusion Detection Using Evolutionary Hidden Markov Model

Intrusion detection systems are responsible for diagnosing and detecting any unauthorized use of the system, exploitation or destruction, which is able to prevent cyber-attacks using the network package analysis. one of the major challenges in the use of these tools is lack of educational patterns of attacks on the part of the engine analysis; engine failure that caused the complete training,  ...

Radar Target Detection Using Hidden Markov Models

Standard radar detection process requires that the sensor output is compared to a predetermined threshold. The threshold is selected based on a-priori knowledge available and/or certain assumptions. However, any knowledge and/or assumptions become inadequate due to the presence of multiple targets with varying signal return and usually non stationary background. Thus, any fixed predefined thres...